Dinesh Ram S

Business analyst training - SAT Connections - Salem

(2025-07 - 2025-08)

Attended structured Business Analyst training focused on requirement gathering, stakeholder communication, and documentation.
Analyzed different perspectives of Business Analysts and customers to understand user-centric approaches.
Developed expertise in creating clear and structured business documentation covering functional and non-functional requirements.
Gained hands-on knowledge of SDLC models, including Agile and Waterfall, to differentiate processes and project needs.
Practiced business process modeling techniques and applied analytical frameworks to interpret stakeholder workflows.
Enhanced requirement documentation through simulations, improving clarity and alignment with project goals.

Data science - Technest intern - Ahmedabad

(2025-06 - 2025-08)

Data Pipeline Development: Designed and implemented automated data pipelines using Python, Pandas, and NumPy for data collection, cleaning, and transformation
Exploratory Data Analysis: Conducted EDA on real-world datasets, handling missing values, duplicates, and visualizing trends using Matplotlib and Seaborn.
Machine Learning Model Evaluation: Explored and evaluated multiple models using metrics like accuracy, precision, recall, and F1-score, improving performance through iterative tuning.
Hyperparameter Tuning: Applied Grid Search and Randomized Search to optimize model performance, reduce overfitting, and enhance predictive accuracy.
Optimization Modeling: Built linear programming models using PuLP/SciPy to solve real-world problems by defining objectives and constraints, improving decision-making.
Deep Learning Projects: Developed neural network models with TensorFlow/Keras, including preprocessing, architecture design, training, and performance evaluation using loss curves and confusion matrices.

Data Science - CodeAlpha - Uttar Pradesh

(2025-05 - 2025-06)

Iris Flower Classification: Built a classification model using supervised learning algorithms to predict flower species based on petal and sepal dimensions.
Unemployment Analysis: Performed exploratory data analysis to identify unemployment trends and patterns using Python libraries like Pandas and Matplotlib.
Car Price Prediction: Developed a regression model to predict car prices based on features such as mileage, brand, and fuel type.
Sales Prediction: Built a predictive model to forecast product sales using historical data, improving understanding of business analytics and data-driven decision-making. [Certificate yet to receive].

Data Science intern - Exposys data labs - Bengaluru

(2024-12 - 2025-01)

Task 1 – Custom Dataset Creation & Model Training. I created my own dataset using multiple data collection methods like web scraping, manual input, and synthetic data generation. After cleaning and preprocessing the data, I trained and evaluated machine learning models using Python libraries such as Pandas, Scikit-learn, and Matplotlib. This helped me understand the complete workflow from raw data to model development.
Task 2 – Diabetes Prediction Project I developed a supervised machine learning model to predict diabetes based on medical features like glucose level, BMI, age, etc. Applied classification algorithms, evaluated performance using accuracy and a confusion matrix, and visualized results. This task deepened my understanding of real-world health data and binary classification.

Data Science - ybi foundation - New Delhi

(2024-08 - 2024-10)

Data Preprocessing & Exploration: Cleaned and prepared the dataset using Python (Pandas, NumPy), handled missing values, encoded categorical features, and performed exploratory data analysis to identify key trends.
Model Development & Evaluation: Built classification models using algorithms like logistic regression and random forest. Evaluated performance using accuracy, precision, recall, and a confusion matrix.

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